technologies

ART8.pdf

Evolutionary theory of technological change: State-of-the-art and new approaches Tessaleno C. Devezas Technological forecasting and Innovation theory Working group, University of Beira Interior, Covilha, Portugal Received 13 may 2004;

accepted 6 october 2004 Abstract It is well known the fact that the world of technology is full of biological metaphors,

and has won the status of a dnatural lawt of technology diffusion due to its considerable success as an empirically descriptive and heuristic device capturing the essential changing nature of technologies, products, markets and industries.

This debate has been in great part centered on the striking similarities between biological evolution and technological/cultural evolution.

and tries to answer the question on the validity of evolutionary models of technological change. After some introductory thoughts in the first part, it is tried in the second part to summarize in five points some of the still missing pieces to complete the puzzle to developing a firmly based Evolutionary theory of technological change (ETTC.

Technological change; Complex systems; Universal Darwinism 1. Introductory thoughts The main objective of this seminar concerns the exploitation of the powerful new capabilities provided by the Information technology Era to advance Future-oriented technology analysis (TFA), both product and process.

Among these new capabilities the TFA Methods Working group has identified recently 1 three main converging areas of development:

following his optimistic view of a strong, confident technology-driven scenario, which would bring a renewed thrust toward new methods in technological forecasting (Fig. 1). The picture suggests that the chaotic phase transition might be behind us

Among the needs for TFA envisioned by the TFA Methods Working group we find the questioning about the validity of the analogy between technological evolution and biological evolution (Ref. 1, pp. 299:

bcan artificial technological worlds be created by simulation modeling analogous to biological ones? Q This question is hardly a new one,

and the manifold convergence of information and molecular technologies that are contributing enormously to new insights in simulation methods and evolutionary programming.

and technological evolution and mostly based on verbal theorizing. It seems that a synthesis of biology

and technology remains beyond reach, with some people even doubting whether it T. C. Devezas/Technological forecasting & Social Change 72 (2005) 1137 1152 1138 can ever be achieved.

are useful metaphors in the realm of economics, business and technology assessment. But few people realize that the inverse is also a common usage:

Theorizing about the evolutionary (Darwinian) aspects of technological change is then not merely a question of using metaphors and making analogies,

and will be seed the and/or the substract for the further development of useful forecasting tools in the technological realm.

technique, technology and (technological innovation. An evolutionary approach within the framework of danthropology of techniquet is a necessary step to grasp adequately these concepts.

I think that some of the above mentioned points are hindering the development of working computational algorithms to simulate technological evolution.

and technology assessment, describing the competition among firms or innovations, or simply among products struggling for a bigger market share.

and Pry 9 demonstrating the validity of the normalized logistic equation in accounting for technological substitution processes or for the diffusion of basic technological innovations.

All this is to say that the use of biological approaches in analyzing the evolution of technology

It is absolutely clear that learning has a definitive role in the technological or cultural evolution (we will turn to this aspect

Here we are dealing also with one of the most controversial points in all previous attempts of comparisons between biological and technological evolution,

and technological realm of the same nature? And if the answer is positive what is the underlying set of rules driving their emergence and continuous unfolding.

and then answering in the positive the question above about the same nature of novelties in the biological, cultural and technological realm:!

The same statement is true in a technological context if we substitute the words dgenetic underpinningst by building blocks T. C. Devezas/Technological forecasting & Social Change 72 (2005) 1137 1152 1143 (following John Holland's 14 original

Again the same statement can be used in the technological realm by substituting words: dgenotypest by any sequence of building blocks, ddifferential reproductive successt by differential adoption in a market and dphenotypet by technical expression. $ My final argument favoring an evolutionary definition of innovation regards the aspect mentioned above of how strongly evolutionary

In despite of the fact that nearly everyone agrees that to explain technological advances we must look beyond the artifacts themselves

1 What should be the suitable unity of analysis in technological evolution? Or in other words, what then actually evolves?

Or the interface of artifacts and ideas in technological practices? 2 How does heritability occur in technological systems?

That is, how do technological units (whatever they may be) carry their information forward through time? 3 Are technological innovations indeed teleological or Lamarckian in nature or not?

Looking at the history of inventions and basic innovations we can find some evident cases of intended

and we can say that a lot of work remains to be done to make evolution a viable strategy and school of thought in the study of technology.

In my view what is missing is a bridge linking evolutionary concepts in biology to technological progress,

but a bridge leading to a level higher up than the plain mapping of every element of technological evolution onto a precise correspondence in the biological counterpart.

T in the way paved by the German philosopher of technology Hans Sachsse 18 almost three decades ago.

1 Technique precedes technology, not only in human history, but also under a pure evolutionary point of view. Technique (or routine,

because nature owns the basic structure (then a fundamental law) of over shortcuts to reach easily the goals immediately ahead. 5 Technology is a recent human achievement that flourished conceptually in the 18th century,

At this point it is worth to point out that I agree with Joel Mokyr 19 that the unity of analysis that makes sense for the study of technological evolution is the dtechnique.

technological evolution as the continuation of biological evolution by other means (or more than blind variation plus selective retention) Karl Popper's 22 view of scientific progress as a cumulative selection process resembling Darwin

's epistemology) and conducting to some conceptual breakthroughs like Richard Dawkins'24 memes in the 1970s and more recently Daniel Dennet's 25 Darwin's Dangerous Idea (the idea that all the fruits of evolution,

Technological evolution (and cultural evolution as a whole) must be subject to more or less analogs of these four forces,

When discussing on the previous points I have pointed already out some features that have not been accounted yet for in the body of existing work on technological evolution.

or genes transmission it had evolved not to technology; if technology had favored not human pool of genes

or human genes transmission it had evolved not continually toward more and more complex technological systems; the human massive capacity for culture (and technology) may be seen as a very strong capacity of adaptation to respond to very quick spatial and temporal variations, observed in the Earth homeland since the Pleistocene;

T. C. Devezas/Technological forecasting & Social Change 72 (2005) 1137 1152 1147 the coevolutionary complexity of managing two inheritance systems (the vertical, genetic,

and the horizontal+vertical, cultural) does not imply necessarily the highest degree of perfection, for we must consider the many cultural pathologies observed in human society.

T technological evolution cannot be thought as an independent evolutionary process, but it is part (the most energetic one) of a broad co-evolutionary set of processes,

some promising approaches As already mentioned there is a relatively vast literature in verbal theories of technological and cultural evolution,

the insistence of trying to map every element of technological evolution onto a precise correspondence in the biological counterpart;

There are two possible approaches to simulating technological and/or socioeconomic systems. The systems dynamics approach, widely used in technological forecasting

and a formal (algorithmically based) model allowing the simulation of technological evolution was developed not yet, there are some attempts following this approach that deserve to be mentioned here.

NK technology landscapes, initially proposed by Stuart Kauffman 26 and further pursued by other researchers of the Santa fe Institute, like Jose'Lobo 27 and Walter Fontana 28;

Scale-free networks pervade technology: the Internet, power-grids and transportation systems are but a few examples. For a review on this field I suggest the reading of two recent review articles 29,30,

and regarding its application to technological systems see the work of Sole'et al. 31, also conducted in close collaboration with other researchers at the Santa fe Institute.

It is a numerical simulation method for the search of complex technology spaces based on percolation theory,

In technology and science GAS have been used as adaptive algorithms for solving practical problems and as computational models of natural evolutionary systems,

and are considered today a relatively mature computational tool for solving complex engineering problems, for which the term Modern Heuristics 36 was coined.

Regarding their use in the simulation of technological evolution it has been used by one of Holland's students

which method is suited best for purposes of simulating technological evolution and/or for developing useful tools for technological forecasting.

Altogether the application of these methods within the limits imposed by their own characteristics has helped researchers in unraveling some until now hidden properties of technological systems.

cultural evolution (and technological evolution as well) is the continuation of biological evolution by other means;

technology must be viewed as the further improvement of this process by intelligent means (then allowing too for intentionality),

human technology is a part of a biologically co-evolved massive capacity for culture, managing two inheritance systems, vertical (twofold in scope, genetic and Lamarckian) and horizontal (pure Lamarckian in scope),

technological evolution is not an independent evolutionary process, but it is the fastest and more energetic among a broad innovation-driven and co-evolutionary set of processes, composing the whole of the world system.

References 1 TFA Methods Working group, Technology futures analysis: toward integration of the fields and new methods, Technol.

model of technological change, Technol. Forecast. Soc. Change 3 (1971) 75 88.10 C. Marchetti, Society as a learning system:

, An Evolutionary theory of Economic Change, Beknap of Harvard university Press, Boston, 1982.17 G. Baslalla, The Evolution of Technology, Cambridge university Press, 1988.18 H. Sachsse, Anthropologie der Technik

, Vieweg, Braunschweig, 1978.19 J. Mokyr, Evolutionary Phenomena in Technological change, in Ref. 15.20 G. M. Hodgson, Darwinism in economics:

extremal search on a technology landscape, SFI-Working Paper 03-02-003,2003. 28 J. Lobo, J. H. Miller, W. Fontana, Neutrality in technological landscapes

Change 71 (2004) 881 896.35 G. Silverberg, B. Verspagen, A percolation model of innovation in complex technology spaces, J. Econ.

ART80.pdf

Adaptive Robust Design under deep uncertainty Caner Hamarat, Jan H. Kwakkel, Erik Pruyt Delft University of Technology policy Analysis Department, PO BOX 5015,2600 GA Delft

A similar attitude is advocated also by Collingridge 33 with respect to the development of new technologies. Given ignorance about the possible side effects of technologies under development, he argues that one should strive for correctability of decisions, extensive monitoring of effects, and flexibility.

More recently, Brans et al. 34 andwalker et al. 24 developed a structured, stepwise approach for dynamic adaptation.

Energy transitions are characterized by many deep uncertainties related to transition mechanisms, to the various competing technologies, and to human and organizational decision-making 45.

Here we focus on the competition between technologies. 3. 1. Introduction to the energy transition case

Energy is a crucial domain inwhich a fundamental transition toward clean generation technologies is desirable 47 for environmental and security reasons.

The current energy systemis mainly dominated by fossil energy generation technologies which are being challenged by rapidly evolving emerging technologies.

Although new sustainable energy technologies are entering the market, their contribution to the total amount of energy generation is still relatively small.

Transition of the energy systemtoward sustainability depends on the developments related to new technologies. Such developments are characterized typically by non-linearity and uncertainty regarding technological characteristics and market adoption 48

49. For example, precise lifetimes of technologies are known not and expected values are used in planning decisions. Also, since the installation of new capacity mostly happens in large chunks,

planning is complex and happens under uncertainty, and construction times are open to surprises affecting the actual completion time.

Other important uncertainties are related to learning effects on costs and technological performance. Costs and technological performance,

and expectations related to them, in turn influence the adoption and survival of technologies during the transition.

These uncertainties play a crucial role and need to be taken into account when analyzing the dynamics of energy transitions

Uncertainties Description Type Range or categories Initial capacities Starting value of the installed capacity of a technology Parametric Varying between 1 and 16,000 MW for different technologies Lifetimes Expected

lifetime of a technology Parametric Varying between 15 and 50 years for different technologies Delay orders of lifetimes Orders of the decommissioning delays Categorical 1st, 3rd, 10th,

1000th Initial decommissioned capacities Initial values of the total decommissioned capacities of the technologies Parametric Varying between 10 and 10,000,

0000 MW for different technologies Planning and construction periods Average period for planning and constructing new capacity for a technology Parametric Varying between 1 and 5 years for different technologies Progress ratios Ratio for determining cost reduction due to learning curve Parametric Varying

between 70%and 95%for four different technologies Initial costs Initial investment cost of new capacity of a particular technology Parametric Varying between €500, 000 and €10 million per MW Economic growth Economic growth

rate Parametric Randomly fluctuating between-0. 01 and 0. 035 (smoothed concatenation of 10-year random growth values) Investment preference structures Preference criteria

and weights for investing in new capacity of each of the technologies Parametric weights and categorical switches Preference for (more) familiar technologies called here the Preference‘Against unknown';

the main structures driving the competition among four energy technologies. Technology 1 represents old dominant nonrenewable technologies.

The other three technologies are at the start of the simulation relatively new, more sustainable, and more expensive.

Since fast and relatively simple models are needed for EMA the more sustainable technologies (2, 3 and 4) are considered to be generic for the sake of simplicity.

The four technologies compete with each other in order to increase their share of energy generation, driven by mechanisms such as total energy demand, investment costs and the effect of learning curves on costs.

A more detailed explanation of the model can be found in 3 . And the uncertainties taken into consideration

The figure shows the behavior over time for the outcome indicator‘fraction of new technologies of total energy generation'as well as the Gaussian Kernel Density Estimates (KDES) 56 of the end states.

These results show that the fraction of new technologies seems to be concentrated around 60%of total generation capacity by the simulated year 2100,

the fraction of new technologies remains below 60%for about half of the runs. If the goal is an energy transition toward sustainability,

To this end, the end states for the total fraction of new technologies are classified as 1

The lower range of the‘lifetime of Technology 1'is relevant for all three subspaces,

i e. the adoption of new sustainable technologies is hampered in combination with the other uncertainties of the subspaces by longer lifetimes of the dominant technology.

Although a low performance of Technology 2 on the‘CO2 avoidance'criterion, a high performance of Technology 1 on the‘expected cost per MWE'criterion

a short lifetime for Technology 3, and a short planning and construction time for Technology 1 also hinder the transition toward sustainability,

none of these uncertainties and their ranges are as unambiguous as the lifetime of Technology 1 (for all regions, not the lower ranges).

Shortening the lifetime of Technology 1 therefore seems to be a promising basic policy, i e. a policy that will be implemented in any case from the start. 3. 2. 2. Basic policy Shortening the lifetime of Technology 1 could be achieved by increasing its decommissioning,

for as long as the fraction of new technologies remains below a particular target fraction, say 0. 8,

assuming that 80%is a reasonable target for the fraction of sustainable technologies. To assess the performance of this basic policy,

the same 9349 experiments used for exploring the no policy case are executed now with the basic policy.

Fig. 4 displays the envelopes spanning the upper and lower limits of the total Fig. 3. Total fraction of new technologies for the‘no policy'ensemble. 413 C. Hamarat et al./

/Technological forecasting & Social Change 80 (2013) 408 418 fraction of new technologies for the no policy ensemble (in blue) and the basic policy ensemble (in green) as well as the KDES of the end states of all

runs in the respective ensembles. The upward shift of the sustainable fraction in Fig. 4 means that the need for new capacity resulting from the additional decommissioning of Technology 1 is to a large extent filled by new technologies.

Hence, the basic policy stimulates the transition from Technology 1 to new technologies, at least to some extent.

Although there is an improvement in terms of the fraction of sustainable technologies there is still a room for further improvement.

Many runs still end below the 60%new technologies threshold. For this reason, we applied PRIM once more with the same classification rule in order to identify troublesome regions for the basic policy.

The basic policy aimed at increasing the decommissioning of the dominant technology, since all PRIM boxes indicated decreasing the negative effect of the lifetime of Technology 1 would help to increase the fraction of new technologies.

The second iteration PRIM results show there are three very different troublesome regions in the basic policy ensemble:

the first region relates to the performance of the technologies on the CO2 avoidance criterion,

the second region relates to the underperformance of Technology 2, and the third region is determined by uncertainties related to economic growth

and expected progress. 3. 2. 3. Contingency planning To redesign and improve the basic policy,

The main drivers of the first region are the CO2 avoidance performance values for Technologies 1, 2,

and 3. If the CO2 avoidance performance of the dominant technology is high, while it is low for the new technologies,

then transition toward new technologies is limited. Additionally, the region shows that higher performance for expected cost per MWE of the dominant technology also limits the transition.

This outcome is not undesirable: it means that the old dominant technology outperforms the other technologies in terms of expected investment costs and CO2 avoidance, which,

in our case (not considering long term security of supply), serves the same goal as the transition.

Hence, it is not necessary to design a strategy for this region; this uncertainty subspace consists of acceptable scenarios in terms of CO2 avoidance

even though the transition to new technologies is limited. The second region is driven mainly by uncertainties related to Technology 2. A shorter lifetime, lower performance of CO2 avoidance,

and longer planning and construction period for Technology 2, lead to low fractions of sustainable technologies.

The results indicate that Technology 1 becomesmore preferable than Technology 2, which is initially themain alternative to Technology 1. In this situation,

a reasonable defensive action would be to focus on the other sustainable technologies, in order to promote the transition toward these technologies instead.

To address this vulnerability a signpost tracking the progress of Technologies 2, 3 and 4 could be used.

The pointwhere the performance of Technology 3 or 4 equals the performance of Technology 2 could be the trigger for Table 2 PRIM results for the no policy ensemble.

Preference against unknown Average planning and construction period Tech. 1 Lifetime of Technology 1 Lifetime of Technology 3 CO2 avoidance performance of Technology 2 Expected

cost per MWE performance of Technology 1 Lower Upper Lower Upper Lower Upper Lower Upper Lower Upper Lower Upper Original 2 5

1 5 30 50 15 40 1 5 1 2 Region1 2 5 1 5 34.4 50 15 37.5 1

4. 2 1. 1 1. 8 Region2 2 5 1 4. 8 33 7 50 15 37.5 1 4. 4

1. 1 2 Region3 2. 9 4. 9 1 4. 5 32.6 50 16.3 40 1 5 1. 2 2

Fig. 4. Comparison of no policy and basic policy for total fraction of new technologies. 414 C. Hamarat

the corrective action would be to stop investing in Technology 2 and to shift investments to Technologies 3 and 4 instead.

So, we modified our basic policy by adding the monitoring and corrective actions and reran the experiments.

Although there is not a clear improvement in terms of total fraction of new technologies, the installed capacities of Technologies 3 and 4 increase.

Thismeans that the defensive action developed for the second region served its purpose by steering the commissioning toward Technologies 3 and 4. The third region shows that certain combinations of economic growth factors

and preference for the expected progress criterion may also hinder the energy transition. Each of the economic growth parameters indicated in the third region corresponds to the value of economic development for ten years

one could conclude that certain combinations of these parameters hinder the breakthrough of new technologies. Since the way in which economic development is represented in this model creates cyclic behavior,

a possible corrective action could be to partly decouple the adoption of new technologies from the economic cyclewith the help of subsidies and additional commissioning of newtechnologies.

we use the investment cost of new technologies as a signpost. A possible defensive action would be to subsidize one

or more sustainable technologies for some time to make them competitive. Hence, the costs of Technologies 2, 3 and 4 are monitored over time

and when their costs are close enough to the cost of the dominant technology, a 20%cost reduction of the new technologies is triggered over a period of 10 years.

To further address this vulnerability, we also add a hedging action to the basic policy in the form of additional commissioning of Technologies 3 and 4 in their early years.

These actions together aim at making the sustainable technologies more cost efficient once their costs are reasonably affordable levels

and to promote the transition toward new technologies in their early years. The economic action is successful in promoting sustainable technologies

and increasing the total fraction after the first 10 years (around 2020). The adoption of the new technologies in later years is also higher than under the basic policy,

suggesting that these cost reductions are effective. To improve the performance of the adaptive policy even further,

the triggers used for adaptivity were optimized using robust optimization 57 59. Using the trigger values optimized over the entire ensemble for the actions previously discussed significantly improves the adaptive policy.

Fig. 5 shows a comparison in terms of the total fraction of new technologies of the‘no policy'ensemble, the‘basic policy'ensemble,

and this‘adaptive policy'ensemble over the same uncertainty space, i e. using the same experimental design.

without forcing a transition to new technologies upon situations that do not require a transition to take place (e g. in case of a cheap and environmentally friendly dominant technology) or for

Fig. 5. Comparison of no policy, basic policy and adaptive policy for total fraction of new technologies. 415 C. Hamarat et al./

and robust policies for socioeconomic and technological changes (i e. energy transitions). This study illustrated the potential of EMA for FTA as suggested by Porter et al. 17.

Policy analysis, Delft University of Technology, Delft, 2010.6 P. Eykhoff, System Identification: Parameter and State Estimation, Wiley Interscience, London, 1974.7 W. E. Walker, V. A w. J. Marchau, D. Swanson, Addressing deep uncertainty using adaptive policies:

I. Miles, M. Mogee, A. Salo, F. Scapolo, R. Smits, W. Thissen, Technology futures analysis: toward integration of the field and new methods, Technol.

Integrating Science and Politics for the Environment, Island Press, Washington, 1993.33 D. Collingridge, The Social control of Technology, Frances Pinter Publisher, London, UK, 1980.34 J. P. Brans

Technology policy and Management, Delft University of Technology, Delft, 2008, p. 285.37 E. Pruyt, J. Kwakkel, A bright future for system dynamics:

Policy 14 (1985) 3 22.49 A. Rip, Introduction of new technology; making use of recent insights from sociology and economics of technology, Technol.

Anal. Strateg. Manag. 7 (1995) 417 431.50 J. W. Forrester, Industrial Dynamics, MIT Press, Cambridge, 1961.51 J. D. Sterman, Business Dynamics:

/Technological forecasting & Social Change 80 (2013) 408 418 Caner Hamarat is a Phd researcher at the Faculty of technology, Policy and Management of Delft University of Technology.

He obtained an MSC degree in Industrial Engineering from Sabanci University. His research interests are exploration and analysis of dynamically complex systems under deep uncertainty.

Jan Kwakkel received a Ph d. from Delft University of Technology. His research focused on the treatment of uncertainty in long-term airport planning.

He currently works as a postdoc on the treatment of uncertainties in model-based decision support for fresh water supply in The netherlands at Delft University of Technology.

Next to this research, he also has an interest in scientometrics and tech-mining. Erik Pruyt is the Assistant professor of System Dynamics and Policy analysis at the Faculty of technology, Policy and Management of Delft University of Technology.

He obtained a master's degree in Commercial Engineering and a Phd degree from the Faculty of economics, Social and Political sciences & Solvay Business school of the Free University of Brussels. His research focuses mainly on the multidimensional dynamics of complex systems,

from short-term crises to long-term transitions. Applied interests include economic-financial crises, climate change and energy system transitions,

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